Depth Recovery Of Monocular Video Based On Machine Learning

Depth recovery of monocular video is an important issue in Computer Vision. Especially,3D movies are more and more popular nowadays and kinds of3D display devices and3D application occupy the market. People are actively going after the3D enjoyment. So the depth recovery of2D video not only reduces the production costs of3D movies, but also gives more visual enjoyment and more3D movies. More and more researchers have focus their concerns on video depth recovery.How to implement depth recovery using machine learning method is a hot topic in recent years. There are lots of researches about this topics having been done and some reasonable algorithms have been proposed. Why concentrated attentions on machine learning are that machine learning method is more general than conventional view geometry method. The results of view geometry method are more accurate and look like much better, but the limitation is that for different input video, we should apply different view geometry approaches using different depth cues.Our method first performs segmentation mask and recoveries the depth map on the key frame by user input, then propagates the segmentation and depth information onto successive frames. We use the inpainting technology to restore the missing pixels’depth, but this method has some limitation in big missing area. So we take machine learning approach to mix up this disadvantage. In our paper, we use SVM method to train learning model and predict the depth information. User’s input will provide lots of guidance information and make the method more general. That’s the reason why we need user’s input.In this paper, we introduce every single implementing detail carefully and the results show that our method outperforms and yields excellent depth maps.